The Impact of Self-Sufficiency in Basic Raw Materials of Metallurgical Companies on Required Return and Capitalization: The Case of Russia

Abstract

This article considers the impact of self-sufficiency in basic raw materials on the level of systematic risk, required return and capitalization on the example of Russian ferrous metallurgy companies. The methods applied include classical approaches to determining beta coefficient, required return and capitalization, as well as correlation–regression analysis performed in the Python programming language (version 3.0, libraries: Numpy, Pandas, Matplotlib, Datetime, Statistics, Scipy, Bambi). The study revealed an inverse relationship between the self-sufficiency of ferrous metallurgy companies in iron ore and coking coal and their systematic risk. That was confirmed by the developed regression model. The presence of this dependence directly indicates the need to consider self-sufficiency when assessing a company’s required return and capitalization. The acquisition of the Tikhov coal mine by PJSC Magnitogorsk Iron and Steel Works (MMK) led to an increase in capitalization not only due to additional profit from the new asset, but also due to a decrease in the required return caused by the growth of the company’s self-sufficiency in coking coal. The proposed approach contributes to a more accurate assessment of the company’s capitalization and creates additional incentives for vertical integration transactions.

1. Introduction

Currently, both the Russian economy as a whole and the metallurgical industry are facing unprecedented challenges (Massel et al., 2024; Prokhorova et al., 2024). Sanctions, loss of premium sales markets and other negative factors lead to increased risks of operations (Cherepovitsyn et al., 2024; Ilyushin & Talanov, 2025; Materova et al., 2024) and a decrease in the financial results of Russian metallurgical companies (Wood Mackenzie, 2024). In such a situation, it is very important for both—corporate management and investors—to maintain a pragmatic and balanced approach not only to assessing opportunities and prospects (Ilyushin et al., 2025), but also to the requirements that investors make of their investment objects (Carnegie, 2024; Pashkevich et al., 2024). In particular, an adequate and comprehensive approach to assessing the required return on investment plays a key role in such a situation. After all, at present it is very easy to overestimate risks and, on the contrary, to underestimate the factors that restrain them, which will find expression in an inflated value of the required return (discount rate) and will lead to a refusal to invest in profitable projects (Afanaseva et al., 2023; Malozyomov et al., 2024; Nevskaya et al., 2024). And this, in turn, will slow down the recovery and further development of one of the most important sectors of the Russian economy, which will negatively affect its prospects as a whole (Semenova & Martínez Santoyo, 2025; Sleptsov et al., 2024; Zhdaneev & Ovsyannikov, 2024).

One of the most serious threats to metallurgical companies is the rise in prices for their basic raw materials—iron ore and coking coal. The cost of these makes up a large part of the cost price (70–80%), and the prices of iron ore and coking coal (as well as other raw materials) depend on the current situation on commodity markets and are very volatile. Thus, by mid-2021, the cost of iron ore futures had increased two and a half times in a little over a year, from USD 80 to USD 210 per ton (Investing.com, 2021). Such a dramatic rise in resource prices threatens the financial results of metallurgical enterprises, which is why this industry is gravitating towards creating vertically integrated companies (Glukhov et al., 2023). In this way, the management of large metallurgical enterprises achieves the provision of production with basic raw materials at the cost of their extraction, processing and transportation (Isheisky et al., 2025; Perveitalov & Nosov, 2025), and not at current market prices, and levels out market volatility.

The above processes create the need to introduce the concept of «self-sufficiency» of companies in basic raw materials, which reflects the share of the cost of production controlled by the vertical integration of the company—in our case, we are talking about the share of the cost of production controlled by the cost of iron ore and coking coal.

The significance of achieving a high level of self-sufficiency in basic raw materials has been well and for a long time recognized by both the researchers (Chvileva & Golovina, 2017; Karpus & Ivashkevich, 2010; Smirnov, 2016) and the management of the companies themselves (Harlanov, 2019; Kostyukhin, 2022). Many Russian metallurgical companies include increasing self-sufficiency in their development strategies as priority areas (for example, (PJSC Severstal, 2023)), which leads to the dominance of vertically integrated corporations in the metallurgical sector. Therefore, the issue of adequate and comprehensive assessment of the impact of measures increasing self-sufficiency on the performance indicators of companies is currently very relevant (Dmitrieva & Solovyova, 2024).

At the same time, this issue is usually considered in the context of the impact of vertical integration on the company’s cash flows, their stabilization by gaining access to reliable sources of raw materials at stable prices (Lebedev & Cherepovitsyn, 2024; Nikolaichuk et al., 2023). However, it seems that the reduction in the risks of metallurgical companies and the growth of stability and predictability of their financial results should influence the economic attractiveness of self-sufficiency projects not only due to the growth and stabilization of projected cash flows, but also due to a reduction in the risk premium when calculating the required return (discount rate) for metallurgical companies.

According to studies (Brounen et al., 2004; Bruner et al., 2008; Gitman & Vandenberg, 2000; Graham & Harvey, 2001; Kolouchová & Novák, 2010; Truong et al., 2008), the main method for determining the discount rate is the Capital Asset Pricing Model (CAPM), the basic principles of which were outlined in the mid-1960s by Sharpe (1964), Lintner (1965) and Mossin (1966). Attempts to adapt the CAPM model for use in countries with low market efficiency have led to the emergence of many modifications: the adjusted local model (Pereiro, 2001), the partial segmentation market model (Bekaert & Harvey, 1995, 2000), the Lessard model (Lessard, 1996), the Godfrey–Espinosa model (Godfrey & Espinosa, 1996), the Damodaran model (Damodaran, 2002a, 2002b), models with the premium for small company size (Banz, 1981; Barry et al., 2002; Mariscal & Lee, 1993), models with a modified risk measure—the Hamada model (Hamada, 1969, 1972) or the Estrada model (Estrada, 1999, 2001, 2002). It should be noted that similar studies were also conducted on the Russian stock market—studies by Bukhlov A.V. (Bukhvalov & Okulov, 2006a, 2006b), Teplova T.V. (Teplova & Shutova, 2011; Teplova & Selivanova, 2010), other authors (Kashina, 2015; Sutyagin et al., 2016; Suvorova et al., 2016), as well as the authors of this publication (Galevskii, 2020; Galevskiy, 2019).

Thus, it seems appropriate to study, first of all, the influence of self-sufficiency of metallurgical companies on the risk assessment in the CAPM. In this model, risk is assessed using the so-called beta coefficient, i.e., a measure of systematic risk. In this regard, the purpose of this study is to determine the influence of self-sufficiency of metallurgical companies on the value of their systematic risk (beta coefficient), required return and capitalization. To achieve this goal, it is necessary to find answers to the following research questions (RQs):

RQ1. 

How to estimate the self-sufficiency of a metallurgical company in basic raw materials?

RQ2. 

How does the level of self-sufficiency of a metallurgical company in basic raw materials affect the level of its systematic risk (beta coefficient)?

RQ3. 

How does the level of self-sufficiency of a metallurgical company in basic raw materials affect the required return and capitalization of the company?

2. Literature Review

A significant number of scientific papers focus on determining the cost of equity capital and quantifying the risk premium in the context of the CAPM model. Some researchers have attempted to estimate the impact of specific systematic risk factors on the beta coefficient (Amiram et al., 2017; Bora et al., 2016; Bora & Vanek, 2017; Chen et al., 2017; Harris & Marston, 2013; Rajhans, 2015).

Thus, Amiram et al. (2017) highlight the importance of forward-looking industry analysis for creditors and identify the most important characteristics based on representative empirical data, including growth, sensitivity to external shocks, industry structure and diversification of the company. Bora et al. (2016) and Bora and Vanek (2017) present an overview and classification of factors influencing the beta coefficient. Peng and Alam (2011) examine the relationship between R&D expenditures and the risk premiums implied in the cost of equity.

For mining and metallurgical companies, there are studies in which their specific activities factors are considered when assessing equity capital. As a rule, in such cases, it is not CAPM models that are used but build-up ones (Michalak, 2014; van Bulck, 2007). Such models can be considered as alternatives for cases where companies are not listed on the stock market, including small businesses (Boudreaux et al., 2011).

The problematic aspects are the ambiguity of the identified risk factors, their number, interrelation, the lack of a single classification and the predicted values of premiums. The build-up approach is based on the consideration of specific risk elements that determine the discount rate in a company. In this case, model development begins with the risk-free rate, to which various combinations of premiums are added that do not have a single justification. For example, premiums for market risk, company size and unsystematic specific risk (Sorin, 2009); total market equity risk premium, size premium, premiums for “unsystematic risk”, country risk and other adjustments (Boudreaux et al., 2011) and others.

A wide range of factors are proposed in the model of the American assessment company Garnett and Hill (G&H), which includes a total of 36 factors (queries) to be assessed. They are divided into four groups of business risks and a group of financial risk factors. Business risk factors include market risk (twelve factors), production risk (six factors), industry risk (four factors) and management risk (six factors); financial risk factors consist of eight subfactors (Bora & Vanek, 2017). There are studies that propose methods to assess factors for the mineral resource and fuel energy companies (Stroykov et al., 2021).

In our opinion, expert assessment of the selection of factors and determination of their values leads to the impossibility of isolating the influence of individual factors, repeated accounting and an unjustified increase in the resulting rate. Therefore, the authors attempted to isolate the influence of a specific factor on the systematic risk of metallurgical companies using statistical methods.

3. Materials and Methods

The structure of the study is presented in Figure 1.

3.1. Sample Selection

To assess the degree of influence of self-sufficiency of ferrous metallurgy companies on their systematic risk, it is necessary to use data on stock quotes of public companies in this industry. Unfortunately, the Russian stock market is characterized by a small number of issuers. Thus, the key index—the MOEX Russia Index (IMOEX)—is formed on the basis of quotes of 45 companies only (MOEX (Moscow Exchange), n.d.-a). Industry indices, in turn, include an even smaller number of issuers. There is no index on the Russian market that reflects the dynamics of shares from the ferrous metallurgy sector, so let us turn to data from the closest index in terms of industry—the MOEX Index, section «Metals and Mining» (MOEXMM) (

Table 1. List of issuers in the MOEX index, section «Metals and Mining» (MOEXMM).

№	Issuer Name	Activity 1	Capitalization, RUB 2 bln	Free-Float
1	PJSC ALROSA	DM	389	0.34
2	PJSC Severstal	FM	1094	0.23
3	EN+ GROUP IPJSC	NFM (Al), E	213	0.14
4	PJSC MMC NORILSK NICKEL	NFM, M (Ni, Pt, Pd, Cu)	1681	0.32
5	PJSC MMK	FM	507	0.2
6	PJSC Mechel	FM, M (CM), E	49	0.43
7	PJSC Mechel (preferred shares)	FM, M (CM), E	17	0.6
8	PJSC NLMK	FM	890	0.21
9	PJSC Polyus	GM	1854	0.22
10	PJSC Raspadskaya	CM	195	0.07
11	United Company RUSAL IPJSC	NFM (Al)	493	0.18
12	PJSC Seligdar	GM	59	0.25
13	PJSC TMK	FM (PP)	124	0.08
14	PJSC UGC	GM	168	0.1
15	PJSC VSMPO-AVISMA Corporation	NFM (Ti)	318	0.1

¹ Abbreviations: DM—diamond mining; FM—ferrous metallurgy, NFM—non-ferrous metallurgy, Al—aluminum, E—energy, M—mining, Ni—nickel, Pt—platinum, Pd—palladium, Cu—copper, CM—coal mining, GM—gold mining, PP—pipe production, Ti—titanium. ² RUB—Russian ruble (Currency). Source: (MOEX (Moscow Exchange), n.d.-b).

) (MOEX (Moscow Exchange), n.d.-b).

As can be seen from the table, even in the industry index, only a few companies can be classified as belonging to the ferrous metallurgy sector: PJSC Severstal, PJSC MMK, PJSC NLMK and PJSC TMK. At the same time, TMK differs significantly from the other three companies in the nature of its products (a highly specialized production facility focused on pipe production), as well as in its small capitalization and small proportion of shares in free float, which casts doubt on the liquidity of the company’s securities and, consequently, the representativeness of their quotes and the beta coefficient calculated on their basis. Therefore, PJSC TMK was excluded from the sample, and the further study is based on the data of the three largest Russian ferrous metallurgy companies: PJSC Severstal, Novolipetsk Iron and Steel Works (PJSC NLMK), and Magnitogorsk Iron and Steel Works (PJSC MMK).

In fact, the formed sample of companies is not exhaustive for obtaining reliable results of the study. In particular, it can be expanded by recruiting ferrous metallurgy companies from other countries, or companies from other industries. However, we believe that in this study it is not reasonable for the following reasons.

• The expansion of the list of Russian ferrous metallurgy companies by foreign ones does not consider the problem of comparability of basic national economic indicators. Inflation levels, government bond yield rates, risk premiums and other indicators differ significantly among the markets of different countries, which affects the values of beta coefficients.
• Inclusion of companies from other industries in the list contradicts the purpose of the study, since the need for the main types of raw materials differs by industry. The main types of raw materials for ferrous metallurgy companies are iron ore and coking coal, while non-ferrous metallurgy companies are subject to significant differentiation depending on the product they produce—for example, in the production of aluminum, the main share of costs falls on alumina and electricity (up to 60%), with electricity alone accounting for up to 40% (Elka Mehr Kimiya, 2025).
• World commodity markets are oligopolies. Even in countries such as the United States and Australia, production is concentrated at the level of a limited number of large national and transnational companies. This fact limits the possibility of expanding the sample.
• Most of the companies listed in paragraph 3 are multi-product, which creates additional difficulties in determining the cost structure for each type of product manufactured and selecting companies in accordance with the purpose of the study.

For the reasons given, we assume that expanding the sample will not improve the validity of the research results. However, a study conducted on a limited sample of companies in a certain country and industry may still be unique and have practical value.

It is also worth noting that this study offers a conceptual approach that has not been previously considered in detail in previous works. The presented sample is largely illustrative. However, additional studies conducted on individual samples of companies from other countries or industries will, with a high degree of probability, improve the reliability of the proposed approach.

3.2. Regression Model Development

At the second stage of the study, a method for assessing the level of self-sufficiency of metallurgical companies was substantiated. As noted in the «Introduction» section, the authors consider the concept of “self-sufficiency” of enterprises in basic raw materials to be the share of the cost of production controlled by the vertical integration of the enterprise, in this case the cost of production controlled by the cost of iron ore and coking coal.

Unfortunately, in Russia there are no unified statistical and analytical databases by economic sectors, on the basis of which it would be possible to reliably calculate the structure of production costs. Based on annual reports of metallurgical companies (PJSC MMK, 2023; PJSC NLMK, 2023; PJSC Severstal, 2023) and secondary analytical sources (Kommersant.ru, 2019; Welfare-economy.com, 2021), it was found that the costs of iron ore and coking coal make up about 75% of the cost of a metallurgical enterprise, with iron ore accounting for 60–65% and coal for 10–15%. Obviously, the cost structure depends on the specific activities of a particular enterprise, but for the purposes of this study, we accept average values, according to which the costs of iron ore make up 62.5% of the cost of production, and the costs of coking coal—125%. Then the level of self-sufficiency can be determined by Formula (1):

$$SR=0.625·IO+0.125·CC,$$

(1)

where

$SR$—level of self-sufficiency,

$IO$—self-sufficiency in iron ore,

$CC$—self-sufficiency in coking coal.

In this case, it can be assumed that there is a relationship between the beta coefficient of a metallurgical company and its level of self-sufficiency, which in general can be described by Formula (2):

$$\beta ={\beta }_0-\alpha ·f\left(SR\right),$$

(2)

where

$\beta$—actual beta coefficient of a vertically integrated metallurgical company,

${\beta }_0$—beta coefficient of a metallurgical company without vertical integration (with a zero level of self-sufficiency),

$\alpha$—coefficient of $f\left(SR\right)$,

$f\left(SR\right)$—a function of the level of self-sufficiency of the metallurgical company.

For the practical application of Formula (2), it is necessary to estimate beta coefficients of the selected Russian metallurgical companies. To calculate the beta coefficients, data on the stock quotes of these companies on the Moscow Exchange, the Moscow Exchange index and the government bond index (as a benchmark for a risk-free asset) were used. Data for 2013–2023 were used for the calculations. Part of the dataset used is presented in

Table 2. Data on the stock quotes of the companies, MCFTR, RGBITR, 2013–2023 (partially).

Date	MCFTR (Moscow Exchange Index)	RGBITR (Russian Government Bonds Index)	Severstal Shares	NLMK Shares	MMK Shares
8 January 2013	1765.99	312.91	389.9	65.38	10.739
9 January 2013	1767.38	314.6	389.7	64.77	10.756
10 January 2013	1757.06	317.48	388	65.3	11.091
11 January 2013	1761.35	316.89	392	65.81	11.079
14 January 2013	1781.92	317.16	397.9	68.14	11.369
…	…	…	…	…	…
25 December 2023	7208.41	614.64	1366.8	195.3	51.97
26 December 2023	7222.22	611.97	1378.8	195.94	52.22
27 December 2023	7142.44	607.88	1356.8	191.22	51.775
28 December 2023	7148.49	610.48	1360	190.94	51.735
29 December 2023	7090.61	611.97	1350.8	189.92	51.4

Source: (MOEX (Moscow Exchange), n.d.-a).

.

This type of dependence (Formula (2)) allows us to take into account the reduction of systematic risks and, as a consequence, the reduction of the beta coefficient of metallurgical companies due to the vertical integration and increased self-sufficiency in basic raw materials. The results of calculations of the dependence using the correlation–regression analysis method are presented in the section «Results and Discussion».

The calculation of the beta coefficients of companies, as well as the correlation–regression dependence describing the relationship between the beta coefficient and the level of self-sufficiency, were performed in the Python programming language (version 3.0) using the Numpy, Pandas, Matplotlib, Datetime, Statistics, Scipy, Bambi libraries.

4. Results and Discussion

Figure 2 shows the dynamics of beta coefficients of the selected companies. Beta coefficients were calculated as five-year moving averages of weekly stock prices of the companies.

As can be seen from Figure 2, in recent years (until early to mid-2022), the beta coefficients of Russian metallurgical companies have been declining, and only with the start of the special military operation, when the risks of the Russian stock market as a whole increased significantly, the values of the beta coefficients of Russian metallurgical companies began to grow, although remaining significantly lower than the levels of five to seven years ago. It seems that the noticeable decrease in beta coefficients in the period 2018–2021 may be caused by a significant increase in the self-sufficiency of the companies in the basic raw materials.

The relationship between the level of self-sufficiency and the level of systematic risk of Russian metallurgical companies is also indicated by the fact that the lowest values of beta coefficients are typical for companies with the best supply of basic raw materials, as can be seen from

Table 3. Levels of self-sufficiency in basic raw materials and beta coefficients of Russian metallurgical companies.

Issuer Name	Iron Ore Self-Sufficiency	Coking Coal Self-Sufficiency	Beta Coefficients
PJSC «Severstal»	130%	80%	0.646
PJSC «NLMK»	90%	100%	0.745
PJSC «MMK»	17%	40%	0.843

Source: compiled and calculated by the authors based on annual reports and stock prices of companies.

(the data presented refers to the end of 2021 to exclude the influence of geopolitical shocks).

As can be seen from Table 3, the highest degree of self-sufficiency is inherent in PJSC Severstal, and the same company has the lowest beta coefficient of all Russian ferrous metallurgy companies. On the contrary, the lowest self-sufficiency and at the same time the highest beta coefficient are inherent in PJSC MMK. It should be noted that Russian metallurgical companies are characterized by such a ratio of self-sufficiency and the level of systematic risk not only as of the end of 2021, but also for the entire observed period, which can be easily seen in Figure 2.

As it is noted in Section 3.1 «Sample Selection» section, the sample of companies is extremely small and cannot be increased without violating the comparability of the data and the study of the Russian case in particular. Then, for the practical implementation of the dependence (Formula (2)), it was not the typical linear regression by the Ordinary Least Squares (OLS) that was implemented, but Bayesian regression using the Student distribution, since this approach shows the following:

• Greater stability in small samples (Gelman et al., 2013; Smid et al., 2020);
• Greater stability to heteroscedasticity of the residuals (Kruschke, 2013; Zuur et al., 2009).

The implementation of this method was carried out in the Python programming language, using the Bambi library. The main parameters of the obtained regression dependence are presented in

Table 4. Parameters of the regression dependence.

Parameter	Mean	Standard Deviation	Low 3%	High 97%
Intercept	0.947	0.108	0.721	1.141
SR	−0.315	0.157	−0.613	−0.003

Source: calculated by the authors in the Python (version 3.0), using the Bambi library (Osvaldo et al., 2024).

.

Despite the wideness of the obtained confidence intervals, it is clear that with a very high probability (more than 97%), the influence of self-sufficiency on the systematic risk of the company is negative, i.e., the higher the self-sufficiency, the lower the beta coefficient. As a result, we obtain the following relationship between the beta coefficient and self-sufficiency (3):

$$\beta =0.947-0.315·SR$$

(3)

The revealed dependence is graphically presented in Figure 3.

The presence of such a dependence shows that the effect of vertical integration of metallurgical companies is often underestimated: only the impact on the cash flows of the business as a whole is considered, but the change in the systematic risk of the company and, as a result, the reduction in the required return (discount rate) are not taken into account.

This can be illustrated by the current example of the acquisition of an important coal asset by the least vertically integrated of Russian metallurgical companies, PJSC MMK, the Tikhov coal mine. According to expert estimates (Finam.ru, 2023), the Tikhov mine will satisfy the growing need of the Magnitogorsk Iron and Steel Works for fatty grades of coking coal and increase self-sufficiency in coal from the current 40% to 50%. According to the same expert estimate, PJSC MMK’s net profit under IFRS is USD 1.3 bln per year, the net profit of the Tikhov coal mine is USD 50 mln per year, or, at the exchange rate current at the time of the study, approximately RUB 114.4 bln and RUB 4.4 bln, respectively.

Let us estimate the level of systematic risk of PJSC MMK shares (beta coefficient) using the obtained regression dependence. Before the acquisition of the Tikhov mine, self-sufficiency in iron ore was 17%, in coal—40%. Accordingly, the level of self-sufficiency of PJSC MMK was

$${SR}_{MMK}=0.625·0.17+0.125·0.4=0.15625$$

Therefore, the beta coefficient of PJSC MMK could be defined as

$${\beta }_{MMK}=0.947-0.315·0.15625=0.898$$

Next, we can estimate the level of required return for PJSC MMK shares using the CAPM model. In this model, in addition to the level of systematic risk (beta coefficient), data on the return on the risk-free asset and the average market return are used, but more often the calculation is carried out using Formula (4):

$$r=r_f+\beta ·ERP,$$

(4)

where

$r$—required return,

$r_f$—return on the risk-free asset,

$\beta$—beta coefficient,

$ERP$—market risk premium.

The yield of long-term government bonds is usually taken as the yield of a risk-free asset; for Russia, this is the yield to maturity of federal loan bonds, which at the time of the study for all issues with a duration of 5–10 years is about 12% per annum (Bank of Russia, 2025). The market risk premium, in turn, is the long-term difference between the average yield on the stock market and the yield on government bonds, which over the past ten years in Russia, according to our estimates, is 8.4% (Damodaran, 2025).

Thus, the required return for PJSC MMK shares is

$$r_{MMK}=12\%+0.898·8.4\%=19.5432\%$$

In turn, having calculated the net profit and the required return on equity, it is possible to estimate the value of PJSC MMK’s business without taking into account the deepening of vertical integration (the acquisition of the Tikhov coal mine):

$$V_{MMK}={\displaystyle \frac{Net Profit}{Required return}}={\displaystyle \frac{114.4}{0.195432}}=585.4 \mathrm{R}\mathrm{U}\mathrm{B} \mathrm{b}\mathrm{l}\mathrm{n}$$

Now let us assume that as a result of the acquisition of the Tikhov mine, the net profit of the entire MMK business will increase, but the risk and, accordingly, the required return will not change. In this case, the value of MMK, taking into account vertical integration, will be

$$V_{MMK}={\displaystyle \frac{114.4+4.4}{0.195432}}=607.9 \mathrm{R}\mathrm{U}\mathrm{B} \mathrm{b}\mathrm{l}\mathrm{n}$$

Certainly, such a calculation involves many assumptions, for example, the constancy of the company’s net profit over time and it is only illustrative. However, even such an approximate estimate gives an adequate result and generally corresponds to the capitalization of MMK, which amounted to about RUB 637 bln on the date of calculations (PJSC MMK, 2025).

Thus, the increase in the value of PJSC MMK’s business due to the acquisition of the Tikhov mine will amount to RUB 22.5 bln, or about 4%, which is consistent with the available data on the transaction amount (Finam.ru, 2023). However, in accordance with this study, not only the company’s net profit should change, but also the required return. Taking into account the acquisition of the Tikhov mine, the level of PJSC MMK’s self-sufficiency should be

$${SR}_{MMK}=0.625·0.17+0.125·0.5=0.16875$$

The changes are generally insignificant (it is changing the supply of coal, but not the main component of the cost of metallurgical companies—iron ore, and it is not changing that significantly), but even these changes should be reflected in the value of PJSC MMK’s beta coefficient:

$${\beta }_{MMK}=0.947-0.315·0.16875=0.893$$

And even such a small change in the beta coefficient leads to a decrease in the required return:

$$r_{MMK}=12\%+0.893·8.4\%=19.5\%$$

Let us calculate the value of MMK’s business taking into account both the increase in net profit and the change in the required return:

$$V_{MMK}={\displaystyle \frac{114.4+4.4}{0.195}}=609.2 \mathrm{R}\mathrm{U}\mathrm{B} \mathrm{b}\mathrm{l}\mathrm{n}$$

5. Discussion

Accordingly, taking into account the change in the required return, the increase in the value of PJSC MMK from the acquisition of the Tikhov coal mine will amount to RUB 23.8 bln. The difference, at first glance, may seem insignificant (RUB 1.3 bln or about 5.5% of the total effect of the acquisition of the Tikhov mine), but even ignoring it in the calculations can lead to an error in making a management decision: abandoning a profitable vertical integration project or choosing its wrong priority direction. Additionally, this case is noticeable and relevant, but still not very large-scale acquisition level, which does not change the level of self-sufficiency of the Magnitogorsk Iron and Steel Works in any noticeable way.

In larger cases, the impact of self-sufficiency in basic raw material on the required return and, as a result, on the business value is much more significant. Thus, the acquisition of Yakovlevsky Mining and Processing Plant (Yakovlevsky GOK) by PJSC Severstal in 2017 (which allowed it to significantly increase self-sufficiency in iron ore (PJSC Severstal, 2025)), with a similar calculation, would have led to a decrease in the required return by almost 0.43%, which would have meant an increase in PJSC Severstal’s capitalization by 2.5%, or by RUB 33 bln in monetary terms only due to a decrease in systematic risk, without taking into account the impact of Yakovlevsky GOK on the company’s cash flows. Obviously, ignoring this effect can significantly distort the assessment results and lead to ineffective management decisions.

The approach to assess the beta coefficient for metallurgical companies proposed by the authors can be interpreted as an option of determining the beta coefficient based on the influencing factors analysis.

In practice, three methods are used to assess the expected beta coefficient:

• Retrospective analysis (collecting and analyzing past data and calculating the “historical” beta coefficient);
• Analog method;
• Influencing factors analysis.

The third method is the least addressed. However, as we have shown in the literature review, a number of influencing factors have been investigated by some authors (Bora & Vanek, 2017). Among the main groups of factors, the influence of (1) operating leverage (related to the structure of operating costs and volatility of operating profit); (2) financial leverage (related to the capital structure and the amount of fees for attracting borrowed funds); and (3) the size of the company (related to the pace of development of the company, the production cycle of the company, the specifics of the goods produced) is highlighted.

The impact of the first two groups of factors (operating and financial leverage) has been the subject of many years of study in financial management and corporate finance. It has analytical insight and multiple empirical confirmation. For example, the authors (Harris & Marston, 2013) proposed an improved approach to assess the market risk premium. The premium considers current market conditions and the relationship between the risk premium for stocks, interest rates and key market risk indicators. Their study was carried out on example of companies in the American stock market.

The paper (Bolek, 2019) shows the relationship between net working capital use strategies and the systematic risk coefficient (beta coefficient). The asset financing strategy reflected in net working capital affects the financial liquidity policy and the company’s risks in the future. The study is based on a quantitative analysis of the non-financial company NewConnect data. The results of the analysis show that the relationship between NWC indicators and the beta coefficient is negative.

In the paper (Melastiani & Sukartha, 2021), it was found that cash flow volatility and the operating cycle had a negative effect on earnings persistence, while sales volatility had a positive effect on earnings persistence.

However, the influence of the third group of factors is ambiguous, since new factors can be involved in the analysis. The literature presents individual studies on specific samples. For example, with respect to the rate of development of a company, the following can be assumed: the higher the growth rate, the higher the risks and volatility of income, and the higher the beta coefficient. It is also logical to assume that the shorter the production (operational) cycle of a company, the lower the beta coefficient should be, since resource consumption is reduced and accelerated, the return on investment is accelerated and operational risks are reduced. These assumptions are related to the general patterns of the firm’s economy.

The impact of the specifics and characteristics of the goods produced may be more debatable: different goods are produced for different markets (B2B, B2C, B2G and more complex combinations), what determines different volumes and structures of demand, prices, government procurement, support, etc. Accordingly, for market and new goods, the risks are higher compared to goods for government needs and goods with a high level of demand.

Other factors may include product and market diversification, access to sources of raw materials and resources, etc.

We believe that our study contributes to the development of the concept of equity capital cost assessment, considering the identified specific operational factor associated with the resource security of a metallurgical company.

6. Conclusions

The following results were obtained in this paper:

• A formula for assessing the self-sufficiency of ferrous metallurgy companies in the basic raw materials (iron ore and coking coal) is proposed.
• The existence of an inverse relationship between the level of self-sufficiency of a metallurgical company and the level of its systematic risk (beta coefficient) is substantiated, and a regression model describing this relationship is obtained.
• It has been demonstrated that the consideration of self-sufficiency is necessary when assessing the required return and capitalization of metallurgical companies, what is especially important in the context of vertical integration transactions. As a rule, the main focus when assessing such transactions falls on additional cash flows from integrated assets. By ignoring the effect of a decrease in the required return, investors may underestimate the incentives to implement these transactions and make unjustified management decisions.

The results obtained in the course of the conducted study have a number of limitations, presented below:

• It is necessary to take into account that the quality of the obtained regression model is limited by a small sample. The reasons for the limited sample size and the lack of opportunity to expand it are presented in detail in Section 3.1 «Sample Selection».
• There is a reason to believe that the obtained dependence has an industry and country linkage; therefore, for companies of another industry or another country of affiliation, it will be necessary to adjust the obtained model.
• The proposed approach is relevant for companies with high material intensity of production, since the influence of self-sufficiency creates any visible effect on the required return and capitalization only with a significant share of material costs in the cost structure.

Considering the results achieved, as well as the limitations of the study, the most promising areas of research are the following:

• Country diversification of the sample. It seems appropriate to test the results of this study on a sample of metallurgical companies from such countries as China (the world leader in ferrous metallurgy), India, Japan and the USA.
• Sectoral diversification of the sample. This is relevant for material-intensive industries such as construction, heavy engineering, chemical and food industries.

Such studies will allow us to clarify the correlation dependencies between the values of self-sufficiency and the beta coefficients of companies. This will expectedly lead to the adaptation of a regression model as applied to a specific country or industry, which will expand the possibilities for the practical application of the proposed approach.